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Narrative Simplification in Human and AI Retelling

Narrative Simplification in Human and AI Retelling

引用

2024 IEEE International Conference on Big Data, BigData 2024

作者： Tanaka, Yuuri Hashimoto, Yasuhiro The University of Aizu Dept. Computer Science & Engineering Aizu-Wakamatsu Japan

ISBN: (纸本)9798350362480

Human beings have long been regarded as "homo narrans,"employing storytelling to comprehend and communicate reality. This study investigates whether contemporary large language models (LLMs) can approximate the dept. and richness of human narrative abilities. Through an experiment comparing retellings by humans and various state-of-the-art LLMs on a dataset of man-made short stories, we examine how word count, vocabulary complexity, and readability evolve during the retelling process. We introduce the Flesch Reading Ease score as a quantitative measure of narrative simplicity. Our findings reveal an inverse correlation between narrative condensation and increased vocabulary density in both human and LLM retellings. While both humans and LLMs tend to condense stories, their approaches differ: humans generally maintain or improve readability, whereas LLMs, without specific instructions on persona-based retellings, often reduce readability. However, with appropriate prompts, LLMs can adjust their storytelling to more closely mimic human tendencies. We also identify a unique "adaptive retelling"ability in humans, who adjust readability based on the original story's readability. These insights advance our understanding of the "AI narrans"concept, highlighting both the current limitations and the potential of LLMs in achieving human-like storytelling capabilities. © 2024 IEEE.

关键词： AI narrans Flesch reading ease score large language models narrative Retelling

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Image Captioning: Analyzing CNN-LSTM and Vision-GPT Models 9

Image Captioning: Analyzing CNN-LSTM and Vision-GPT Models

引用

9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Karthik, Abburi Sai Karthik, Maddala H. S. M. Krishna Yashwanth, Samudrala Anjali, T. Amrita Vishwa Vidyapeetham Dept. of Computer Science And Engineering Amritapuri India

ISBN: (纸本)9798350394474

Image captioning, which exists at the point of intersection of computer vision and natural language processing, is essential for enhancing image comprehension, allowing applications like content discovery, visual aid for the blind, and more. The hunt for more precise and reliable picture captioning models continues to be an important research goal as technology develops quickly. The two prominent image captioning techniques used in this study Image Captioning Using LSTM+CNN and Image Captioning Using VisionGPT2 are thoroughly compared. We examine these models' internal workings, assess their effectiveness, and offer insights into their advantages and disadvantages for diverse application *** neural networks (CNNs) for extracting visual features and long short-term memory (LSTM) networks for producing sequential language are combined in the LSTM+CNN model, a tried-and-true methodology. It has shown adept.in creating insightful descriptions for a variety of photographs. On the other hand, VisionGPT2, a GPT-2 architectural extension, makes use of transformers and pretrained language models to provide cutting-edge outcomes in a range of natural language processing applications. We analyze the viability of each technique by taking into account elements like model complexity, training data needs, and deployment simplicity. This comprehensive comparison enlightens academics, programmers, and businesses on the ideal picture captioning solution for their particular requirements, fostering development in this area and its numerous uses. © 2024 IEEE.

关键词： Image enhancement

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DynaCache: A Checkpoint Aware Reconfigurable Cache for Intermittently Powered Computing Systems 32

DynaCache: A Checkpoint Aware Reconfigurable Cache for Inter...

引用

32nd IFIP/IEEE International Conference on Very Large Scale Integration, VLSI-SoC 2024

作者： Mahanta, Rishabh Kapoor, Hemangee K. Indian Institute of Technology Dept. of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331539672

Energy harvesting devices are rapidly evolving to rival battery-backed technologies. Batteries have a shorter life- time and need maintenance compared to capacitors. Moreover, the usage of batteries comes with undeniable environmental costs. Distinct challenges have emerged such as performance enhancement, crash consistency of data, energy management, etc. To tackle these challenges we propose DynaCache: A checkpoint-aware reconfigurable cache for Intermittent powered computing systems. It is an intermittent computing architecture consisting of a reconfigurable L1 cache that dynamically trans- forms its write policy to strike a favorable trade-off between per- formance, data consistency, and forward progress of a program. Our proposal achieves a speedup in performance of 1.85x and 1.5x compared to non-volatile write-back cache and volatile write- through cache respectively. It also achieves a 99% reduction in dirty data compared to a volatile write-back cache guaranteeing a near absolute data consistency in a scenario of a power failure. © 2024 IEEE.

关键词： Cache memory

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Ocular Disease Detection using Machine Learning 8

Ocular Disease Detection using Machine Learning

引用

8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Bangera, Vaishali Rajashree Bhagawath, Srilaxmi Raja Shetty, Srujana A Spoorthi, B. Nmamit Dept. of Computer Science Nitte Karkala India Sit Dept. of Computer Science Valachil Mangaluru India

ISBN: (纸本)9798350367904

The prevention of visual impairment or blindness is largely dependent on early detection of ocular illnesses, which pose a serious worldwide health burden. Using a carefully selected dataset from Kaggle, this study investigates the use of machine learning techniques for the automated detection of ocular disorders. The dataset is made up of a wide range of retinal images that have been labeled with details on different eye disorders. The goal is to find a deep learning model that is reliable and precise enough to analyze retinal images and categorize them into several disease categories, such as glaucoma, macular degeneration, diabetic retinopathy, and normal conditions. A comparative analysis between different deep learning models like CNN, BCNN, VGG16 and VGG19 is performed in order to find the model that results in higher efficiency, that is, the model that best fits the dataset for multiocular disease detection. The idea of multi-disease detection was also proposed in the year 2021, which set a benchmark by gathering huge amount of relevant data for the study. It differentiated itself from the previous findings that where focused on predicting single ocular disease. Hence this research is a advancement in multi-disease detection where VGG16 proved to be the best fitting model for each label, with efficiency of 9 6 %. © 2024 IEEE.

关键词： Adversarial machine learning

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Leukaemia: A Comparative Analysis of Deep Learning Models Using ALL Dataset 6

Leukaemia: A Comparative Analysis of Deep Learning Models Us...

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6th IEEE International Conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Sudheer, Rhea Vivek, Rapaka Ramesh, Richa Sarvade, Apoorva Kakarla, Sada Preethi, P. Pes University Dept. of Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9798350381689

White blood cells, also known as leukocytes, or WBCs, spread abnormally in the bone marrow and blood, resulting in leukaemia (blood cancer). Leukaemia can be identified by pathologists by examining a patient's blood sample under a microscope. By counting different blood cells and physical characteristics, they can identify and classify leukaemia. This method takes a lot of time to forecast leukaemia. The pathologist's professional qualifications and experiences may also have an impact on this process. Traditional machine learning and deep learning techniques in computer vision are useful road maps that improve the precision and speed of identifying and categorizing medical images, such as minuscule blood cells. This paper offers a thorough analysis of the different Deep CNN models for identification and classification of WBCs and acute leukaemia in microscopic blood cells. © 2024 IEEE.

关键词： Contrastive Learning

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Utilizing Genetic-Neural System Fusion for Advanced Prediction and Classification of Damage Intensity in Road Traffic Accidents 2

Utilizing Genetic-Neural System Fusion for Advanced Predicti...

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Rahi, Parvez Kang, Sandeep Singh Singh, Ajay Pal Singh, Inderjeet Chandigarh University Dept. of Computer Science & Engineering Punjab Mohali India

ISBN: (纸本)9798350371314

This study proposes an analysis and prediction framework for road traffic accidents (RTAs) in the hilly terrain of Himachal Pradesh and several states in, India, integrating machine learning techniques such as neural networks (NNs) and genetic algorithms (GAs). Utilizing a comprehensive dataset comprising historical accident records, geographical features, weather conditions, and demographic information specific to Himachal Pradesh, the framework aims to identify patterns and correlations contributing to RTAs. Neural networks are employed to discern complex relationships within the data, while genetic algorithms optimize model parameters for enhanced predictive accuracy. By forecasting accident-prone areas and periods, this approach enables proactive interventions and targeted road safety measures. The outcomes of this research provide valuable insights for stakeholders and policymakers, facilitating the reduction of RTAs and mitigating their impact in the challenging terrain of Himachal Pradesh including all states of India. © 2024 IEEE.

关键词： Genetic algorithms

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COVID-19 Image Retrieval Using Siamese Deep Neural Network and Hashing Technique 14

COVID-19 Image Retrieval Using Siamese Deep Neural Network a...

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14th International Conference on Information and Knowledge Technology, IKT 2023

作者： Boroujeni, Farsad Zamani Afshari, Doryaneh Hossien Mahmoodi, Fatemeh Science and Research Branch Dept. of Computer Engineering Tehran Iran Branch Dept. of Computer Engineering Isfahan Iran

ISBN: (纸本)9798350349412

Medical images are crucial for early diagnosis and are increasingly important in modern medicine. This study presents a medical image retrieval system using a Siamese neural network with 13 layers. The system uses the Minimum Redundancy Maximum Relevance (mRMR) technique to extract deep features from the Siamese, and then uses binary hashing to retrieve similar images using Hamming distance. The experimental results on Covid-19 dataset show that the proposed method improves lung image retrieval by over 6% compared to previous methods, achieving an average accuracy of 93.83% and 92.73% in 5 and 10 retrieved images, respectively. © 2023 IEEE.

关键词： Image enhancement

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Identification of Sugarcane Leaf Diseases and Deficiency Disorders Using Transformers 1

Identification of Sugarcane Leaf Diseases and Deficiency Dis...

引用

1st International Conference on Pioneering Developments in computer science and Digital Technologies, IC2SDT 2024

作者： Sarkar, Subhasish Kumar, Gautam National Institute of Technology Dept. of Computer Science and Engineering Delhi India

ISBN: (纸本)9798350365016

Early identification of plant diseases and nutrient deficiencies is crucial for ensuring healthy food production, especially for crops like sugarcane, which contribute significantly to our food supply. Farmers often struggle to differentiate between diseases and deficiencies due to similar symptoms. To overcome such challenges, this study leverages the application of transformers to classify whether the given input is suffering from disease or deficiency. For this purpose, two transformers, i.e., Vision Transformer (ViT) and Swin Transformer is trained on the dataset having leaves of sugarcane. The performance of the model is calculated in terms of precision, recall, accuracy and F-score. From the results, it is found that the Vision transformer with an accuracy of 99.6% can classify diseases and deficiencies more accurately compared to the 99.2% accuracy for Swin transformer. © 2024 IEEE.

关键词： Plant diseases

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GNN-based Disease Detection and Classification in Cassava Leaf 5

GNN-based Disease Detection and Classification in Cassava Le...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Meenalochini, M. Amalroshini, A. Nandhini, K L Megha Murugan, C R Sogil Kongu Engineering College Dept. of Computer Science and Design Erode India

ISBN: (纸本)9798331518097

Cassava, a staple crop for millions of people worldwide, is highly susceptible to various leaf diseases, which can significantly reduce crop yields. Detecting and classifying these diseases at an early stage is crucial for effective disease management and crop protection. In this paper, we propose a novel approach for cassava leaf disease classification by combining Convolutional Neural Networks (CNNs) and Graph Neural Networks (GNNs). While CNNs excel at extracting local features from images, they often fail to capture complex spatial relationships between different regions of the leaf. To address this limitation, we integrate GNNs, which are well-suited for learning from irregular, structured data, such as the vein patterns and disease spread structures found in cassava leaves. By modeling the relationship between different regions of the cassava leaf as a graph G (V, E), where V represents nodes corresponding to superpixel regions of the leaf, and E represents edges capturing spatial relationships, the hybrid CNN-GNN model offers a more comprehensive and accurate classification system. The model is trained and evaluated on a dataset of cassava leaf images covering multiple disease categories, demonstrating superior performance over traditional CNN-based image classification techniques. This approach provides a scalable solution for improving the detection and management of cassava leaf diseases. © 2024 IEEE.

关键词： Convolutional neural networks

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A Comparative Analysis of Machine Learning Prediction Algorithms for Detecting IoT Botnet Activities

A Comparative Analysis of Machine Learning Prediction Algori...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Happy Chhikara, Rita Kashyap, Neeti The NorthCap University Dept. of Computer Science and Engineering Haryana Gurugram India

ISBN: (纸本)9798350375237

As the prevalence of Internet of Things (IoT) products keeps increase, the need for robust security measures becomes increasingly vital. Our paper addresses this concern by conducting a detailed comparative analysis of various machine learning methods, assessing their effectiveness in detecting and predicting malicious activities associated with IoT botnets. This paper meticulously examines initial identification methods for IoT botnet operations using advanced machine learning (ML) prediction techniques. To achieve this, we utilize the CICIoT2023 Dataset, a real-world IoT dataset obtained from networks that captures diverse device interactions and communication patterns. This dataset acts as the framework for constructing and evaluating numerous machine learning techniques, including support vector machines (SVM), k-nearest neighbours (k-NN), Naive Bayes, random forest (RF), logistic regression (LR), and decision trees (DT) approaches. Performance metrics such as accuracy, precision, F1-score, ROC curve, confusion matrix, and recall are employed to gain insights into the algorithms' capabilities in botnet detection. Furthermore, this paper delves into an examination of the trade-offs between computational complexity and detection accuracy. This analysis aids in selecting the most suitable ML techniques tailored to specific IoT security scenarios. This comparative analysis lays the groundwork for the advancement of IoT botnet discovery strategies, providing essential insights to researchers, practitioners, and industry experts working to strengthen IoT ecosystems against growing cyber threats. We anticipate that our findings will spark more conversations and developments in the sector, promoting the establishment of more robust and adaptable security measures across the IoT landscape. © 2024 IEEE.

关键词： Internet of things

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